Related methods such as TRILL[1]}, COLA[2]}, and Fonseca et al.[3]} learn representations by comparing input segments cropped from audio clips. These learn to make closer embeddings for segments closer in time, while pushing away embeddings for remote segments. Though different segments could have different details, these encourage representations to become closer for the closer segments, even ignoring the details.
In contrast, we propose to learn representations of the audio segment itself with details.
